Financial Returns in Shares - Free Essay Example

Sample details Pages: 3 Words: 1025 Downloads: 10 Date added: 2017/06/26 Category Finance Essay Type Narrative essay Did you like this example? Here are the descriptive statistics for the following four shares: Allied Domecq Aviva Barclays BOC Group Allied Don’t waste time! Our writers will create an original "Financial Returns in Shares" essay for you Create order Aviva Barclays BOC Mean 0.034927 0.019586 0.053065 0.041042 Standard error 0.016635 0.018953 0.01532 0.017607 Median 0.022258 0.014174 0.054806 0.034762 Mode #N/A #N/A #N/A #N/A Standard Deviation 0.083174 0.094767 0.076599 0.088034 Sample Variance 0.006918 0.008981 0.005867 0.00775 Range 0.323539 0.440406 0.315845 0.367016 Minimum -0.09075 -0.24905 -0.10914 -0.12904 Maximum 0.232789 0.191359 0.206701 0.237978 Sum 0.873169 0.489661 1.326634 1.026045 Count 25 25 25 25 From the above statistics I can draw the following comments. Looking at the mean I can see that returns from Aviva are the lowest. The highest average returns are from Barclays. However, all average returns seem to be at a similar level. Looking at the median supports this observation. The median shows Aviva having the lowest returns and Barclays having the highest. There is no data for the mode, but all this means is that no return is the same as another. Looking at the standard deviation and range I can see that returns from Aviva seem to be the most variable as Aviva has the biggest deviation and the biggest range. Barclays seems to have the least variable returns, as the standard deviation and the range for this share are the lowest of the four. In conclusion, I can say that Barclays has the best returns and Aviva has the lowest returns. Looking at this histogram I can say that it appears to be symmetrically distributed. The average return seems to be in the middle and returns lower at either end. This chart looks to be roughly symmetrical. The only difference to the chart above id the fact that the middle value is low. Also, there is one value well away from the other values. This chart again seems to be symmetrical. However, there are 3 values with the same frequency. This makes the chart more positively skewed than the others. This chart is again pretty much symmetrically distributed. In this chart there are more values than the other charts. This could mean that this share has greater range. Looking at all the charts I can say that the common return is between 0.5 and 0.1. This seems to be the average for all shares. Correlation: Allied Aviva Barclays BOC Allied 1 Aviva 0.234587 1 Barclays 0.222791 0.343281 1 BOC 0.203977 0.038567 0.258946 1 Looking at the above correlation table I can say that all the shares have positive correlation with each other, however, the correlation is low. The highest correlation seems to be with Aviva and BOC. The lowest correlation is between Allied and BOC. There would appear to be very little correlation between these shares. This is probably because of the fact that each company is in a different industry. The following table shows the statistics for the Jarque-Bera test: Allied 22.8406 Aviva 8.227033 Barclays 16.18868 BOC 8.631524 The test statistic I am using is at the 99% confidence level and is 10.6. Thus, the null hypothesis that the distribution is normally distributed will be rejected if the Jarque-Bera statistic is higher than 10.6. From the above table I can see that Aviva and BOC are normally distributed and Barclays and Allied are not. In terms of market efficiency this has great levels of implication. Models of market efficiency such as EMH state that returns are based on a normal distribution. If the Jarque-Bera statistic is stating that returns are not normally distributed then EMH models can not be accurately used to estimate the future returns of such share prices. However, if share prices that are not normally distributed can be predicted with a high degree of accuracy then this could mean that markets are not as efficient as first thought. It could also imply that the models and theories used to estimate market efficiency are not as accurate and reliable as always thought. It could mean that new models and theories have to be thought up. I have looked at the relationship between the FTSE all share and Allied. I decided that Allied is the dependant variable and the FTSE all share is the independent variable. This is because the returns of Allied will be dependant upon the movement of the FTSE all share coupled with the beta of Allied. The beta of a company is a figure that determines how risky it is compared to the market in which it operates. A high beta will mean that the company follows the market closely and a low beta means the company follows the market only very slightly. A positive beat means that the company positively follows the movement of the market and a negative beta means that the company will move in the opposite direction of the market. The beta of a company can be worked out by performing a regression model with the returns of the stock as the dependant variable and the returns of the market as the independent variable. The following market model is generated: Return Allied = 0.013297 + 0.557 return FTSE all share The model states that Allied has a beta of 0.557. This means that it is positively correlated with the FTSE all share. When the FTSE all share goes up Allied will also go up at about 55.7% of the movement of the FTSE all share. However, if the FTSE all share moves down, Allied will also fall, but only at 55.7% of the overall fall of the FTSE all share. To work out the values needed for the following chart I used the returns of FTSE all share and placed them into the market model equation in part 3 to generate the returns for the market model. I then plotted the returns from the FTSE 100 in the same chart and generated the following graph: Looking at the above chart I can see that the returns from the market model closely follow the FTSE 100. In fact a movement of approximately a half by the market model mirrors each movement of the FTSE 100 line. This can be explained by the fact that the market model has a beta of 0.557. This means any movement in the FTSE 100 will mean the market model moving in the same direction, but only at 55.7% of the FTSE 100. The movements of the market model will probably not be exactly 55.7% as this model was generated using the FTSE all share. The movements in the FTSE all share will be slightly different to the FTSE 100, which will distort the effects of the above chart slightly.